Multicarrier technologies have recently been a subject of research. Orthogonal frequency division multiplexing (OFDM) technology has been utilized in wireless local area network (WLAN) systems (IEEE 802.11 standard). The point in OFDM is to multiplex a high data rate information signal into several low data rate subcarrier signals for transport over a radio channel. An advantage of this technology is that frequency selective fading of an information signal can be effectively avoided, since each subcarrier experiences relatively flat fading.
Other multicarrier technologies have been topics of intense study. Such technologies include multicarrier code division multiple access (MC-CDMA) technology, multicarrier direct sequence (DS) CDMA technology, and multitone (MT) CDMA technology. Utilization of one or several of these technologies for example in a fourth generation mobile telecommunication system has been studied.
A disadvantage of multicarrier systems, and especially OFDM systems, is their sensitivity to frequency-offset errors of subcarriers. Frequency offset results in intercarrier interference (ICI) at demodulator outputs. Frequency offset may be caused by a mismatch of local oscillators in a transmitter and a receiver. The mismatch is due to non-idealities of the oscillators. Additionally, frequency offset of subcarriers is also introduced in a radio channel, where the offset may be caused for example by a Doppler frequency shift, which is often present in mobile communication environments.
In order to negate or minimize effects of frequency offset, a frequency-offset synchronization is essential in a receiver. Usually, frequency-offset synchronization is implemented using a frequency offset estimation algorithm providing an estimate of the frequency offset. The local oscillator of the receiver is then adjusted according to the frequency offset estimate.
Known algorithms for estimating frequency-offset errors include an extended Kalman filter (EKF), which is a recently established algorithm. The extended Kalman filter is a first order, non-linear filter, whose estimation accuracy depends mainly on the stability of a Jacobian matrix. For example, in the presence of large Doppler spread, the Jacobian matrix may become numerically unstable, which is why the estimate produced by the EKF may diverge. Therefore, more powerful non-linear filters such as a Gaussian sum particle filter, a particle EKF (PEKF), and unscented Kalman filter (UKF) have been proposed. Particle filter based structures, however, still require the Jacobian matrix, and the UKF performs poorer than the EKF in general.
Further information on the EKF can, if necessary, be obtained in literature, for instance in Kim Kyeong Jin et al: Joint Detection and Channel Estimation Algorithms for QS-CDMA Signals Over Time-Varying Channels, IEEE Transactions on Communications, vol. 50, pp. 845-855, May 2002. More information on the Gaussian sum particle filter can be obtained in Kotecha J. H. et al: Gaussian Sum Particle Filtering for Dynamic State Space Models, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing 2001, pp. 3465-3468, May 2001. More information on the PEKF can be obtained in Kim Kyeong Jin et al: A Sequential Monte-Carlo Kalman Filter based Delay and Channel Estimation Method in the MIMO-OFDM System, Proceedings of IEEE Vehicular Technology Conference, 2004. More information on the unscented Kalman filter can be obtained in Wan E. A. et al: The Unscented Kalman Filter for Nonlinear Estimation, Proceedings of IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium, pp. 153-158, October 2000. These publications are incorporated herein by reference.